A Machine Learning Approach to Generate Rules for Process Fault Diagnosis
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- Shastri Srinivas
- School of Engineering, Murdoch University
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- Lam Chiou-Peng
- School of Computer and Information Science, Edith Cowan University
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- Werner Brenda
- School of Engineering, Murdoch University
Bibliographic Information
- Other Title
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- Machine Learning Approach to Generate Rules for Process Fault Diagnosis
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Abstract
Expert systems can play a very important role in manufacturing processes by locating problems as soon as they arise. The most important ingredient in any expert system is knowledge. The current knowledge acquisition method is slow and tedious and there exist substantial difficulties in acquiring the knowledge for complex processes. An approach is proposed that makes use of the machine learning technique, C4.5, to generate a decision tree. The decision tree is translated into rules that are implemented into the expert system shell, G2. The rules are tested using a sensitivity analysis of the system. The approach works well, but depends on both the quality and quantity of available training data.
Journal
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- JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 37 (6), 691-697, 2004
The Society of Chemical Engineers, Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001204568039168
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- NII Article ID
- 10013340627
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- NII Book ID
- AA00709658
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- COI
- 1:CAS:528:DC%2BD2cXlsVOntLc%3D
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- ISSN
- 18811299
- 00219592
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- NDL BIB ID
- 6981492
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed